The present study endeavors to explore the dynamic causal relationship between economic growth and financial development in Kuwait, covering the time span between 1991 and 2017. Based on the objective of presenting robust results in relation to the research focus, a combination of time and frequency-domain methodologies has been applied. Using the Toda–Yamamoto and the Fourier Toda–Yamamoto time-domain techniques, no causal direction is found through the former test, while the causality flowing from financial development to economic growth is demonstrated by the latter. Nevertheless, the spectral causality test developed by Breitung and Candelon [(2006). “Testing for short- and long-run causality: A frequency-domain approach.” Journal of Econometrics 132 (2): 363–378. https://doi.org/10.1016/j.jeconom.2005.02.004] gives a more thorough overview of the dynamic causal relationships because it allows the temporary and permanent movements in the linkages between variables to be differentiated. The findings show proof of a bidirectional causality between financial development and economic growth. Our findings highlight the emphasis on covering the frequency causality to provide greater insight into the interrelationship between the variables under consideration.

Early diagnosis of retinal OCT images has been shown to curtail blindness and visual impairments. However, the advancement of ophthalmic imaging technologies produces an ever-growing scale of retina images, both in volume and variety, which overwhelms the ophthalmologist ability to segment these images. While many automated methods exist, speckle noise and intensity inhomogeneity negatively impacts the performance of these methods. We present a comprehensive and fully automatic method for annotation of retinal layers in OCT images comprising of fuzzy histogram hyperbolisation (FHH) and graph cut methods to segment 7 retinal layers across 8 boundaries. The FHH handles speckle noise and inhomogeneity in the preprocessing step. Then the normalised vertical image gradient, and it’s inverse to represent image intensity in calculating two adjacency matrices and then the FHH reassigns the edge-weights to make edges along retinal boundaries have a low cost, and graph cut method identifies the shortest-paths (layer boundaries). The method is evaluated on 150 B-Scan images, 50 each from the temporal, foveal and nasal regions were used in our study. Promising experimental results have been achieved with high tolerance and adaptability to contour variance and pathological inconsistency of the retinal layers in all (temporal, foveal and nasal) regions. The method also achieves high accuracy, sensitivity, and Dice score of 0.98360, 0.9692 and 0.9712, respectively in segmenting the retinal nerve fibre layer. The annotation can facilitate eye examination by providing accurate results. The integration of the vertical gradients into the graph cut framework, which captures the unique characteristics of retinal structures, is particularly useful in finding the actual minimum paths across multiple retinal layer boundaries. Prior knowledge plays an integral role in image segmentation.

The paper assessing the nature of corporate accounting regulation in developing countries in general and in Libya in particular. It examines IASs/IFRSs adaption in these countries

Optical Coherence Tomography (OCT) is one of the most vital tools for diagnosing and tracking progress of medication of various retinal disorders. Many methods have been proposed to aid with the analysis of retinal images due to the intricacy of retinal structures, the tediousness of manual segmentation and variation from different specialists. However image artifacts, in addition to inhomogeneity in pathological structures, remain a challenge, with negative influence on the performance of segmentation algorithms. In this paper we present an automatic retinal layer segmentation method, which comprises of fuzzy histogram hyperbolization and graph cut methods. We impose hard constraints to limit search region to sequentially segment 8 boundaries and 7 layers of the retina on 150 OCT B-Sans images, 50 each from the temporal, nasal and center of foveal regions. Our method shows positive results, with additional tolerance and adaptability to contour variance and pathological inconsistence of the retinal structures in all regions.